A synthetic neural system is an information processing paradigm that is inspired by the way biological nervous systems, such as the brain, process information. Biological systems inspire system design in many other ways as well, for example reflex reaction and health signs, nature inspire systems (NIS), hive and swarm behavior, and fire flies. These synthetic systems provide an autonomic computing entity that can be arranged to manage complexity, continuous self-adjustment, adjustment to unpredictable conditions, and prevention of and recovery for failures.
One key element is the general architecture of the synthetic neural system. A synthetic neural system is composed of a large number of highly interconnected processing autonomic elements that are analogous to neurons in a brain working in parallel to solve specific problems. Unlike general purpose brains, a synthetic neural system is typically configured for a specific application and sometimes for a limited duration.
Synthetic neural systems derive meaning from complicated or imprecise data and are used to extract patterns and detect trends that are too complex to be noticed by either humans or other computer techniques. A trained synthetic neural system can be thought of as an “expert” in the category of information it has been given to analyze. This expert can then be used to provide projections given new situations of interest and answer “what if” questions. Synthetic neural systems, like people, may learn by example. They may be adapted, changed and reconfigured through a learning process in which results are compared to goals and objectives, and changes are made to the synthetic neural system to conform future results of the synthetic neural system to those goals and objectives. Learning in both biological systems and synthetic neural systems involves adjustments to connections between the “neurons.”
Often, autonomic elements monitor and direct each other to some extent. In conventional situations, the data that the autonomic elements monitor and use to direct can be somewhat sparse. Sparse data can increase the likelihood of incorrect directions.
In addition, systems that receive data from autonomic units can experience processing delays because the data is not in a format that can be quickly processed. Under certain challenging circumstances, the processing delay can have severe detrimental consequences.
For the reasons stated above, and for other reasons stated below which will become apparent to those skilled in the art upon reading and understanding the present specification, there is a need in the art for autonomic elements to communicate a richer set of data to each other. There is also a need to reduce processing delays in data sent from autonomic units.